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Kandinsky Figures and Kandinsky Patterns are mathematically describable, simple self-contained hence controllable test data sets for the development, validation and training of explainability in artificial intelligence.
Über die Formfrage
Wassily Kandinsky · 1912
Earlier work this paper cites.
Principles of Gestalt Psychology
Kurt Koffka · 1935
Earlier work this paper cites.
Die Logik der Forschung. Zur Erkenntnistheorie der modernen Naturwissenschaft
Karl Popper · 1935
Earlier work this paper cites.
Laws of organization in perceptual forms
Max Wertheimer · 1938
Earlier work this paper cites.
Chapter 2: On attributes and concepts
Jerome S. Bruner · 1956
Earlier work this paper cites.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
David H. Hubel and Torsten N. Wiesel · 1962
Earlier work this paper cites.
Concept learning: An information processing problem
Earl B. Hunt · 1962
Earlier work this paper cites.
Machine learning
Tom M. Mitchell · 1997
Earlier work this paper cites.
Computational intelligence: a logical approach
David Lynton Poole, Alan K. Mackworth, and Randy Goebel · 1998
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Bayesian modeling of human concept learning
Joshua B. Tenenbaum · 1999
Cited alongside, same era.
Wassily Kandinsky, 1866-1944: A Revolution in Painting
Hajo Düchting · 2000
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Learning a classification model for segmentation
Xiaofeng Ren and Jitendra Malik · 2003
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Supervised learning of edges and object boundaries
Piotr Dollar, Zhuowen Tu, and Serge Belongie · 2006
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The iat shows no evidence for kandinsky’s color-shape associations
Alexis Makin and Sophie Würger · 2013
Cited alongside, same era.
Learning like a child: Fast novel visual concept learning from sentence descriptions of images
Junhua Mao, Xu Wei, Yi Yang, Jiang Wang, Zhiheng Huang, and Alan L. Yuille · 2015
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 2016
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick · 2017
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Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2017
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Human-level concept learning through probabilistic program induction
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
What do we need to build explainable ai systems for the medical domain?
Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis, and Douglas B. Kell
Cited in the paper.
Andreas Holzinger, Markus Plass, Katharina Holzinger, Gloria Cerasela Crisan, Camelia-M. Pintea, and Vasile Palade
Cited in the paper.
Mémoire sur les probabilités
Pierre-Simon Laplace
Cited in the paper.
Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
Later among the works it cites.
Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Causability and explainability of ai in medicine
Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo Mueller · 2019
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